Informative knowledge distillation for image anomaly segmentation

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989 · see citing works

Nearest neighbor pattern classification

Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936 · see citing works

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works

Deep High-Resolution Representation Learning for Visual Recognition

Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983686 · Cited by 4,894 · see citing works

Knowledge Distillation: A Survey

Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931 · see citing works

FitNets: Hints for Thin Deep Nets

Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.6550 · Cited by 2,859 · see citing works

Deep Learning for Anomaly Detection: A Review

Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837 · see citing works

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627 · see citing works

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930 · see citing works

Deep One-Class Classification

Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719 · see citing works

Deep Learning for Anomaly Detection: A Review

Pang, G., Shen, C., Cao, L., et al. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.2007.02500 · Cited by 1,718 · see citing works

A review of novelty detection

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649 · see citing works

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,561 · see citing works

PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization

Defard, T., Setkov, A., Loesch, A., & Audigier, R. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-68799-1_35 · Cited by 1,415 · see citing works

Similarity-Preserving Knowledge Distillation

Tung, F., & Mori, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00145 · Cited by 1,179 · see citing works

Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00424 · Cited by 990 · see citing works

International Conference on Pattern Recognition

(1971) · Computer · doi:10.1109/c-m.1971.216793 · Cited by 828 · see citing works

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., & Steger, C. T. (2019) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0007364503720380 · Cited by 819 · see citing works

Reconstruction by inpainting for visual anomaly detection

Zavrtanik, V., Kristan, M., & Skočaj, D. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107706 · Cited by 752 · see citing works

CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

Huang, Y., Wang, Y., Tai, Y. Y., Liu, X., Shen, P., Li, S., Li, J., & Huang, F. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00594 · Cited by 607 · see citing works

The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., & Steger, C. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01400-4 · Cited by 602 · see citing works

Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation

Yi, J., & Yoon, S. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-69544-6_23 · Cited by 551 · see citing works

Working hard to know your neighbor's margins: Local descriptor learning lossPreprint

Mishchuk, A., Mishkin, D., Radenović, F., & Matas, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10872 · Cited by 540 · see citing works

Deep Anomaly Detection Using Geometric TransformationsPreprint

Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479 · see citing works

Surface Defect Detection Methods for Industrial Products: A Review

Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457 · see citing works

Student-Teacher Feature Pyramid Matching for Anomaly Detection

Wang, G., Han, S., Ding, E., & Huang, D. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.349 · Cited by 394 · see citing works

Learning Discriminative Reconstructions for Unsupervised Outlier Removal

Xia, Y., Cao, X., Wen, F., Hua, G., & Sun, J. Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.177 · Cited by 297 · see citing works

PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

Reiss, T., Cohen, N., Bergman, L., & Hoshen, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00283 · Cited by 295 · see citing works

adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

Wang, X., Du, Y., Lin, S., Cui, P. Y., Shen, Y., & Yang, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105187 · Cited by 145 · see citing works

Learning Student Networks via Feature Embedding

Chen, H., Wang, Y., Xu, C., Xu, C., & Tao, D. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2970494 · Cited by 116 · see citing works

Information Processing in Medical Imaging

Sun, H., Zhen, X., Bailey, C., Rasoulinejad, P., Yin, Y., & Li, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59050-9 · Cited by 115 · see citing works

Iterative energy-based projection on a normal data manifold for anomaly localizationPreprint

Dehaene, D., Frigo, O., Combrexelle, S., & Eline, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03734 · Cited by 112 · see citing works

Student-Teacher Feature Pyramid Matching for Anomaly DetectionPreprint

Wang, G., Han, S., Ding, E., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04257 · Cited by 105 · see citing works

Notice of Removal: Multiresolution Knowledge Distillation for Anomaly Detection

Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H., & Rabiee, H. R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01466 · Cited by 97 · see citing works

Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature

Wan, Q., Gao, L., Li, X., & Wen, L. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3094452 · Cited by 94 · see citing works

Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

Rippel, O., Mertens, P., Konig, E., & Merhof, D. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3098381 · Cited by 84 · see citing works

Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition

Yan, Y., Wang, D., Zhou, G., & Chen, Q. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3107586 · Cited by 60 · see citing works

Knowledge transfer in fault diagnosis of rotary machines

Liu, G., Shen, W., Gao, L., & Kusiak, A. (2022) · IET Collaborative Intelligent Manufacturing · doi:10.1049/cim2.12047 · Cited by 43 · see citing works

Unsupervised Anomaly Detection Using Style Distillation

Chung, H., Park, J.-H., Keum, J., Ki, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3043473 · Cited by 35 · see citing works

Self-Attention Autoencoder for Anomaly SegmentationPreprint

Yang, Y. (2021) · Preprints.org · doi:10.20944/preprints202108.0570.v1 · Cited by 2 · see citing works

Deep Industrial Image Anomaly Detection: A Survey

Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463 · see citing works

A Diffusion-Based Framework for Multi-Class Anomaly Detection

He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., & Xie, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28690 · Cited by 245 · see citing works

AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

Hu, T., Zhang, J., Yi, R., Du, Y., Chen, X., Liu, L., Wang, Y., & Wang, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28696 · Cited by 166 · see citing works

AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

Cao, Y., Zhang, J., Frittoli, L., Cheng, Y., Shen, W., & Boracchi, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_4 · Cited by 165 · see citing works

Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

Cao, Y., Xu, X., Liu, Z., & Shen, W. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3241579 · Cited by 149 · see citing works

A survey of deep learning for industrial visual anomaly detection

Li, Z., Yan, Y., Wang, X., Ge, Y., & Meng, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11287-7 · Cited by 145 · see citing works

PNI: Industrial Anomaly Detection using Position and Neighborhood Information

Bae, J., Lee, J.-H., & Kim, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00586 · Cited by 127 · see citing works

Complementary pseudo multimodal feature for point cloud anomaly detection

Cao, Y., Xu, X., & Shen, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110761 · Cited by 119 · see citing works

Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping

Costanzino, A., Ramirez, P. Z., Lisanti, G., & Stefano, L. D. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01631 · Cited by 104 · see citing works

CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-Shot Anomaly Detection

Chen, X., Zhang, J., Tian, G., He, H., Zhang, W., Wang, Y., Wang, C., & Liu, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9003-6_2 · Cited by 101 · see citing works

BMAD: Benchmarks for Medical Anomaly Detection

Bao, J., Sun, H., Deng, H., He, Y., Zhang, Z., & Li, X. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00408 · Cited by 100 · see citing works

Exploring plain ViT features for multi-class unsupervised visual anomaly detection

Zhang, J., Chen, X., Wang, Y., Wang, C., Liu, Y., Li, X., Yang, M., & Tao, D. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104308 · Cited by 81 · see citing works

A comprehensive survey for real-world industrial surface defect detection: Challenges, approaches, and prospects

Cheng, Y., Cao, Y., Yao, H., Luo, W., Jiang, C., Zhang, H., & Shen, W. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.11.022 · Cited by 69 · see citing works

Dual-Attention Transformer and Discriminative Flow for Industrial Visual Anomaly Detection

Yao, H., Luo, W., Yu, W., Zhang, X., Qiang, Z., Luo, D., & Shi, H. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3322156 · Cited by 66 · see citing works

Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection

Cao, Y., Xu, X., Cheng, Y., Sun, C., Du, Z., Gao, L., & Shen, W. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3536165 · Cited by 60 · see citing works

A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection

Jiang, Y., Cao, Y., & Shen, W. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110982 · Cited by 51 · see citing works

AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection

Wu, Q., Li, H., Tian, C., Wen, L., & Li, X. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.001 · Cited by 48 · see citing works

LogiCode: An LLM-Driven Framework for Logical Anomaly Detection

Zhang, Y., Cao, Y., Xu, X., & Shen, W. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3468464 · Cited by 45 · see citing works

REB: Reducing biases in representation for industrial anomaly detection

Lyu, S., Mo, D., & Wong, W. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111563 · Cited by 38 · see citing works

Incremental Template Neighborhood Matching for 3D anomaly detection

Wang, J., Wang, X., Hao, R., Yin, H., Huang, B., Xu, X., & Liu, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127483 · Cited by 37 · see citing works

Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation

Jin, Y., Peng, J., He, Q., Hu, T., Wu, J., Chen, H., Wang, H., Zhu, W., Chi, M., Liu, J., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02832 · Cited by 37 · see citing works

Two-stage reverse knowledge distillation incorporated and Self-Supervised Masking strategy for industrial anomaly detection

Tong, G., Li, Q., & Song, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110611 · Cited by 36 · see citing works

BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization

Cao, Y., Xu, X., Sun, C., Gao, L., & Shen, W. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3344383 · Cited by 35 · see citing works

Enhanced Multi-Scale Features Mutual Mapping Fusion Based on Reverse Knowledge Distillation for Industrial Anomaly Detection and Localization

Tong, G., Li, Q., & Song, Y. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3350539 · Cited by 29 · see citing works

Attention-Gate-based U-shaped Reconstruction Network (AGUR-Net) for color-patterned fabric defect detection

Zhang, H., Wang, S., Lu, S., Yao, L., & Hu, Y. (2023) · Textile Research Journal · doi:10.1177/00405175221149450 · Cited by 28 · see citing works

A Survey of Methods for Automated Quality Control Based on Images

Diers, J., & Pigorsch, C. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01822-w · Cited by 26 · see citing works

Yolo-sd: simulated feature fusion for few-shot industrial defect detection based on YOLOv8 and stable diffusion

Wen, Y., & Wang, L. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02175-7 · Cited by 25 · see citing works

Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection

Liu, X., Wang, J., Leng, B., & Zhang, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681669 · Cited by 24 · see citing works

Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection

Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22 · see citing works

Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection

Hoang, D.-C., Tan, P. X., Nguyen, A.-N., Tran, D.-T., Duong, V.-H., Mai, A.-T., Pham, D.-L., Phan, K.-T., Do, M.-Q., Duong, T. H. A., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3525567 · Cited by 22 · see citing works

Unsupervised anomaly detection and localization with one model for all category

Tan, P., & Wong, W. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111533 · Cited by 22 · see citing works

MCAD: Multi-classification anomaly detection with relational knowledge distillation

Li, Z., Ge, Y., Yue, X., & Meng, L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09838-0 · Cited by 22 · see citing works

Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection

Jiang, Y., Cao, Y., & Shen, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3463495 · Cited by 21 · see citing works

Position Encoding Enhanced Feature Mapping for Image Anomaly Detection

Wan, Q., Cao, Y., Gao, L., Shen, W., & Li, X. (2022) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49997.2022.9926547 · Cited by 20 · see citing works

Fusion-restoration model for industrial multimodal anomaly detection

Wang, J., Niu, Y., & Huang, B. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130073 · Cited by 17 · see citing works

Dual-path Frequency Discriminators for few-shot anomaly detection

Bai, Y., Zhang, J., Chen, Z., Dong, Y., Cao, Y., & Tian, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112397 · Cited by 17 · see citing works

MemFormer: A memory based unified model for anomaly detection on metro railway tracks

Liu, R., Liu, W., Duan, M., Xie, W., Dai, Y., & Liao, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121509 · Cited by 16 · see citing works

Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

Yao, H., Luo, W., Zhang, W., Zhang, X., Qiang, Z., & Luo, D. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109235 · Cited by 16 · see citing works

Model compression optimized neural network controller for nonlinear systems

Li, L.-J., Zhou, S.-L., Chao, F., Chang, X., Yang, L., Yu, X., Shang, C., & Shen, Q. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110311 · Cited by 15 · see citing works

LightCTS*: Lightweight Correlated Time Series Forecasting Enhanced With Model Distillation

Lai, Z., Zhang, D., Li, H., Jensen, C. S., Lu, H. C., & Zhao, Y. L. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3424451 · Cited by 14 · see citing works

Fast and efficient computing for deep learning-based defect detection models in lightweight devices

Fișne, A., Kalay, A., & Eken, S. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02487-z · Cited by 14 · see citing works

Dual-student knowledge distillation for visual anomaly detection

Hao, J., Huang, K., Chen, C., & Mao, J. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01412-4 · Cited by 13 · see citing works

MGSGNet-S*: Multilayer Guided Semantic Graph Network via Knowledge Distillation for RGB-Thermal Urban Scene Parsing

Zhou, W., Wu, H., & Jiang, Q. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3456437 · Cited by 13 · see citing works

Unsupervised industrial anomaly detection using paired well-lit and low-light images

Hoang, D.-C., Tan, P. X., Nguyen, A.-N., Pham, M.-K., Duong, T. H. A., Huynh, T.-M., Bui, S.-A., Nguyen, D.-M., Ha, Q. P., Trinh, V.-A., et al. (2025) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaf043 · Cited by 12 · see citing works

Anomaly Detection for Medical Images Using Teacher–Student Model With Skip Connections and Multiscale Anomaly Consistency

Liu, M., Jiao, Y., Lü, J., & Chen, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3406792 · Cited by 12 · see citing works

Semi-supervised pipeline anomaly detection algorithm based on memory items and metric learning

Yan, B., Zheng, J., Li, R., Fu, K., Chen, P., Jia, G., Shi, Y., Lv, J., & Gao, B. (2023) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2023.2169285 · Cited by 12 · see citing works

A novel anomaly detection and classification algorithm for application in tuyere images of blast furnace

Duan, Y., Xiaojie, L., Liu, R., Li, X., Li, H., Li, H., Sun, Y., Zhang, Y., & Lv, Q. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109558 · Cited by 11 · see citing works

Self-Supervised Autoencoders for Visual Anomaly Detection

Bauer, A., Nakajima, S., & Müller, K. (2024) · Mathematics · doi:10.3390/math12243988 · Cited by 11 · see citing works

Skip-ST: Anomaly Detection for Medical Images Using Student-Teacher Network with Skip Connections

Liu, M., Jiao, Y., & Chen, H. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10181639 · Cited by 10 · see citing works

InViT: GAN Inversion-Based Vision Transformer for Blind Image Inpainting

Du, Y., Liu, H., He, S., & Chen, S. (2024) · IEEE Access · doi:10.1109/access.2024.3447055 · Cited by 9 · see citing works

A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model

Sajid, M., Khan, A. H., Malik, K. R., Khan, J. A., & Alwadain, A. S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2944 · Cited by 9 · see citing works

Deep Feature Contrasting for Industrial Image Anomaly Segmentation

Wan, Q., Cao, Y., Gao, L., Li, X., & Gao, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3348901 · Cited by 9 · see citing works

Test Time Training for Industrial Anomaly Segmentation

Costanzino, A., Ramirez, P. Z., Moro, M. D., Aiezzo, A., Lisanti, G., Salti, S., & Stefano, L. D. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00395 · Cited by 8 · see citing works

Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection

Aqeel, M., Sharifi, S., Cristani, M., & Setti, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_28 · Cited by 8 · see citing works

Adaptive Context-Aware Distillation for Industrial Image Anomaly Detection

He, Y. J., Yang, H. S., & Yin, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3336758 · Cited by 8 · see citing works

AnomalyControl: Few-Shot Anomaly Generation by ControlNet Inpainting

Ali, M., Fioraio, N., Salti, S., & Stefano, L. D. (2024) · IEEE Access · doi:10.1109/access.2024.3520002 · Cited by 7 · see citing works

Industrial anomaly detection with multiscale autoencoder and deep feature extractor‐based neural network

Tang, T., Hsu, H., & Li, K. (2023) · IET Image Processing · doi:10.1049/ipr2.12752 · Cited by 7 · see citing works

3D multimodal feature for infrastructure anomaly detection

Jing, Y., Lin, W., Sheil, B. B., & Acikgoz, S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106388 · Cited by 6 · see citing works

KD-KI: Knowledge distillation with knowledge infusion for anomaly detection and localization

Huang, W., Xu, Z., Wan, R., Yang, X.-H., & Zhang, B. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131423 · Cited by 6 · see citing works

MGSCA: Multiscale Gradient Selection-Based Coupled-Hypersphere Adaptation for Unsupervised Pavement Defect Detection

Zhang, Y., Li, Z., Lai, T., Li, W., & Zheng, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3370756 · Cited by 6 · see citing works

Unsupervised anomaly detection and localization via bidirectional knowledge distillation

Wang, X., Wang, Y., Pan, Z., & Wang, G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10172-8 · Cited by 6 · see citing works

MiniMaxAD: A lightweight autoencoder for feature-rich anomaly detection

Wang, F., Liu, C., Shi, L., & Pang, H. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104315 · Cited by 6 · see citing works

Improving generalization in deep neural network using knowledge transformation based on fisher criterion

Morabbi, S., Soltanizadeh, H., Mozaffari, S., & Fadaeieslam, M. J. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05448-0 · Cited by 6 · see citing works

Robust AD: A Real World Benchmark Dataset for Robustness in Industrial Anomaly Detection

Pemula, L., Zhang, D., & Dabeer, O. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00389 · Cited by 5 · see citing works

CFFDist: Cross-Scale Feature Fusion Distillation Network for Industrial Anomaly Localization

Zhi, H., Qin, H., Zhang, L., Guo, J., & Song, B. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3502773 · Cited by 5 · see citing works

Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation

Li, J., Li, M., Huang, S., Wang, G., & Zhao, X. (2025) · Sensors · doi:10.3390/s25123721 · Cited by 4 · see citing works

A novel dual-student reverse knowledge distillation method for magnetic tile defect detection

Tang, J., Zhang, A., & Liu, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-12339-2 · Cited by 3 · see citing works

A feature shuffling and restoration strategy for universal unsupervised anomaly detection

Luo, W., Yao, H., Qiang, Z., Zhang, X., & Zhang, W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114874 · Cited by 3 · see citing works

Masked feature regeneration based asymmetric student–teacher network for anomaly detection

Gu, H., Li, G., & Liu, Z. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18512-5 · Cited by 3 · see citing works

Domain knowledge-driven encoder–decoder for nasopharyngeal carcinoma segmentation

Xu, G.-X., Ren, C.-X., & Sun, Y. H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125208 · Cited by 3 · see citing works

Outlier Exposure in Visual Industrial Anomaly Detection - Survey

Shi, Y. H., Xu, R., & Qi, Z. (2024) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw65004.2024.00008 · Cited by 3 · see citing works

Discriminating feature ratio: Introducing metric for uncovering vulnerabilities in deep convolutional neural networks

Szandała, T., & Maciejewski, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112306 · Cited by 3 · see citing works

Teacher Probability Reconstruction based knowledge distillation within intelligent network compression

Chen, H., Teng, X., Su, J., Li, C., Hu, C., & Han, M. (2025) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2025.02.001 · Cited by 3 · see citing works

High-Resolution Image Anomaly Detection via Spatiotemporal Consistency Incorporated Knowledge Distillation

Cao, Y., Zhang, Y., & Shen, W. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260338 · Cited by 3 · see citing works

Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection

Yao, H., Luo, W., Cao, Y., Zhang, Y., Yu, W., & Shen, W. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3595539 · Cited by 3 · see citing works

Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts

Hou, Y., Xu, K., Li, J., Ruan, Y., & Qiu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_4 · Cited by 2 · see citing works

Unsupervised Anomaly Detection on Preclinical Liver H &E Whole Slide Images Using Graph Based Feature Distillation

Li, L., Shelton, L. E., Forest, T. W., Janardhan, K. S., Jenkins, T., Napolitano, M. J., Wang, R., Leigh, D. A., Shah, T., Carlson, G. E., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_64 · Cited by 2 · see citing works

SD-GASNet: Efficient Dual-Domain Multi-Scale Fusion Network with Self-Distillation for Surface Defect Detection

Fu, J., Zhang, Z., Peng, T., Hu, X., & Zhang, J. (2025) · Sensors · doi:10.3390/s26010023 · Cited by 2 · see citing works

IAENet: An importance-aware ensemble model for 3D point cloud-based anomaly detection

Cao, X., Tao, C., Cheng, Y., & Du, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104097 · Cited by 2 · see citing works

A 3D anomaly detection method based on multiscale point features for defects in vehicle stamped parts

Liu, Y., Zhang, C., Dong, X., & Yang, Y. (2026) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2026.01.102 · Cited by 2 · see citing works

RoCA: Robust Contrastive Adaptation for unsupervised anomaly detection

Tan, X., Chen, C., Gao, H., Yu, Y., & Liang, R. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113843 · Cited by 2 · see citing works

Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application

Yu, R., Guo, B., & Li, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02095 · Cited by 2 · see citing works

3D-FPFH-Int: a hybrid geometric–radiometric descriptor for structural surface anomaly detection in tropical heritage

Gbran, H., & Murtiono, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-59034-4 · Cited by 2 · see citing works

Unsupervised Prediction of Blastocyst Development from Oocyte Images

García-de-la-Puente, N. P., Payá, E., Murria, L., López-Pérez, M., Meseguer, M., & Naranjo, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78241-1_19 · Cited by 2 · see citing works

Abnormal-Aware Loss and Full Distillation for Unsupervised Anomaly Detection Based on Knowledge Distillation

Zhao, M., & Song, Y. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222157 · Cited by 2 · see citing works

Unsupervised industry anomaly detection via asymmetric reverse distillation

Sun, X., Pan, W., Qin, J., Lang, Y., & Qian, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109759 · Cited by 2 · see citing works

Integrating statistical significance and discriminative power in pattern discovery

Alexandre, L., Costa, R. S., & Henriques, R. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113356 · Cited by 2 · see citing works

Out-of-distribution detection in 3D applications: A review

Li, Z., Kang, X., West, J., & Khoshelham, K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134503 · Cited by 2 · see citing works

Learning to Be a Transformer to Pinpoint Anomalies

Costanzino, A., Ramirez, P. Z., Lisanti, G., & Stefano, L. D. (2025) · IEEE Access · doi:10.1109/access.2025.3582900 · Cited by 1 · see citing works

A Hierarchical Patch Feature Distribution Network for Industrial Multiscale Defect Detection

Zheng, Z., Yang, H. S., & Yin, Z. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3598017 · Cited by 1 · see citing works

Few-shot anomaly detection and classification through reinforced data selection with a combinatorial reward

Han, X., Xu, D., Yuan, S., & Wu, X. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02572-6 · Cited by 1 · see citing works

UMIAD-EGMF: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion

Li, Z., Lin, G., Zhang, D., Huang, R., & Yang, J. (2026) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-026-00215-3 · Cited by 1 · see citing works

Few-shot medical anomaly detection through centroid consultation back and test-time self-calibration

Nie, Z., Xu, M., Cui, Y., Wei, H., Yi, W., Niu, S., Wan, Y., Wei, X., & Song, W. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113261 · Cited by 1 · see citing works

Bidirectional adaptive transformers for multimodal anomaly detection

Jiang, Y., Cao, Y., & Shen, W. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132129 · Cited by 1 · see citing works

Enhanced Reverse Distillation Guided Segmentation Network for Anomaly Detection

Li, X., Ma, X., Chen, J., Xu, Q., & Yang, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10649917 · Cited by 1 · see citing works

Extreme Inception Deep Learning Model for Multi-Factor Car Dent Severity Assessment and Repair Cost Estimation

Jawale, J., Shah, K., & Balsara, D. D. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115532 · Cited by 1 · see citing works

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly ConsistencyPreprint

Liu, M., Jiao, Y., Lü, J., & Chen, H. (2023) · doi:10.36227/techrxiv.24330880.v1 · Cited by 1 · see citing works

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly ConsistencyPreprint

Liu, M., Jiao, Y., Lü, J., & Chen, H. (2023) · doi:10.36227/techrxiv.24330880

Reverse Distillation Support Vector Data Description for Unsupervised Anomaly Detection

Liu, Z., Wu, G., & Lan, J. (2023) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1145/3652628.3652640

Image Anomaly Detection via Reverse Informative Distillation

Liu, M., Zhang, S., & Duan, K. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10587782

A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection

Jiang, Y., Cao, Y., & Shen, W. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110982 · Cited by 51 · see citing works

Abnormal-Aware Loss and Full Distillation for Unsupervised Anomaly Detection Based on Knowledge Distillation

Zhao, M., & Song, Y. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222157 · Cited by 2 · see citing works

Unsupervised anomaly detection and localization via bidirectional knowledge distillation

Wang, X., Wang, Y., Pan, Z., & Wang, G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10172-8 · Cited by 6 · see citing works

Semantic-Guided Denoising Knowledge Distillation Model for Anomaly Detection

Zhang, J., Guo, N., Wang, J., Li, C., & Liu, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5696-0_27

ADPS: Asymmetric Distillation Postsegmentation for Image Anomaly Detection

Xing, P., Tang, H., Tang, J., & Li, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3390806 · Cited by 19 · see citing works